An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large data sets, such as three-dimensional medical images. We propose an adaptive sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in improved learning. Our contribution is threefold: 1) We give a detailed description of the proposed sampling algorithm to speed up and improve learning performance on large images. We propose a deep dual path CNN that captures information at fine and coarse scales, resulting in a network with a large field of view and high resolution outputs. We show that our method is able to attain new state-of-the-art results on the VISCERAL Anatomy benchmark

On the Average Comoving Number Density of Halos

I compare the numerical multiplicity function given in Yahagi, Nagashima & Yoshii (2004) with the theoretical multiplicity function obtained by means of the excursion set model and an improved version of the barrier shape obtained in Del Popolo & Gambera (1998), which implicitly takes account of total angular momentum acquired by the proto-structure during evolution and of a non-zero cosmological constant. I show that the multiplicity function obtained in the present paper, is in better agreement with Yahagi, Nagashima & Yoshii (2004) simulations than other previous models (Sheth & Tormen 1999; Sheth, Mo & Tormen 2001; Sheth & Tormen 2002; Jenkins et al. 2001) and that differently from some previous multiplicity function models (Jenkins et al. 2001; Yahagi, Nagashima & Yoshii 2004) it was obtained from a sound theoretical background

Adjuvants : an essential component of neisseria vaccines

Adjuvants may be classified into delivery systems and immune potentiator or modulator molecules based on their mechanism of action. Neisseria vaccines containing traditional adjuvants such as aluminium salts have existed for long time, but meningitis caused by Neisseria meningitidis serogroups, particularly serogroup B, continues to be a global health problem. Novel strategies have applied in silico and recombinant technologies to develop "universal" antigens (e.g. proteins, peptides and plasmid DNA) for vaccines, but these antigens have been shown to be poorly immunogenic even when alum adjuvanted, implying a need for better vaccine design. In this work we review the use of natural, detoxified, or synthetic molecules in combination with antigens to activate the innate immune system and to modulate the adaptive immune responses. In the main, antigenic and imune potentiator signals are delivered using nano-, micro-particles, alum, or emulsions. The importance of interaction between adjuvants and antigens to activate and target dendritic cells, the bridge between the innate and adaptive immune systems, will be discussed. In addition, nasal vaccine strategies based on the development of mucosal adjuvants and Neisseria derivatives to eliminate the pathogen at the site of infection provide promising adjuvants effective not only against respiratory pathogens, but also against pathogens responsible for enteric and sexually transmitted diseases

Ajuste de modelos não-lineares aos dados de crescimento de búfalas da raça Murrah criadas em terras baixas no estado do Rio Grande do Sul.

ZOOTEC 2009

Curvature in causal BD-type inflationary cosmology

We study a closed model of the universe filled with viscous fluid and quintessence matter components in a Brans-Dicke type cosmological model. The dynamical equations imply that the universe may look like an accelerated flat Friedmann-Robertson-Walker universe at low redshift. We consider here dissipative processes which follow a causal thermodynamics. The theory is applied to viscous fluid inflation, where accepted values for the total entropy in the observable universe is obtained.Comment: 11 pages, revtex 4. For a festschrift honoring Alberto Garcia. To be publishen in Gen. Rel. Gra

Parâmetros genéticos estimados por análise bayesiana para peso e escore de precocidade ao sobreano de bovinos brangus.

ZOOTEC 2009

k-strings and baryon vertices in SU(N) gauge theories

It is pointed out that the sine law for the k-string tension emerges as the critical threshold below which the spatial Z_N symmetry of the static baryon potential is spontaneously broken. This result applies not only to SU(N) gauge theories, but to any gauge system with stable k-strings admitting a baryon vertex made with N sources in the fundamental representation. Some simple examples are worked out.Comment: 4 pages, 4 figures, v2: reference added, v3: comments and references adde